Analyzing the Role of Community and Individual Factors in LAMP Grant Funding: Identifying Diverse Barriers Across Clustered US Counties

FAS Food Systems Impact Fellowship Capstone Project, April 2024

Author

Elliot Hohn Sr. Agricultural Data Scientist Impact Fellow

Introduction

Local Agriculture Market Program (LAMP)

The USDA’s Agricultural Marketing Service (AMS) administers a variety of grant programs aimed at strengthening local and regional food systems. The Local Agriculture Market Program (LAMP) is one such program that supports direct producer-to-consumer marketing, food enterprises, and value-added agricultural products. Established under the 2018 Farm Bill, LAMP fosters community collaboration and public-private partnerships to improve regional food economies, aiding in the development of business strategies and infrastructure for local food systems. The Farm Bill provided LAMP $50 million per year in mandatory funding and the programs received significant supplemental funding through the Consolidated Appropriations Act of 2021 and the American Rescue Plan of 2021.1 The major grant programs within LAMP include the Local Food Promotion Program (LFPP), Regional Food Systems Partnership (RFSP), and the Farmers Market Promotion Program (FMPP).

Promotional materials for LAMP. Image: USDA-AMS, 2024

Building community capital through food systems investment

Allocating grant funding

The goals of the LAMP program include: (1) simplify the application processes and the reporting processes for the Program; (2) improve income and economic opportunities for producers and food businesses through job creation; and (3) strengthen capacity and regional food system development through community collaboration and expansion of mid-tier value chains.2

Each program within LAMP includes a set of constraints intended to improve the allocation of resources to specific program activity areas.

In 2021, AMS partnered with Florida A&M University and the University of Maryland Eastern Shore on a project focusing on the following goals3:

  1. Evaluate barriers to AMS grant opportunities for socially disadvantaged communities

  2. Invest in building trust and confidence between these communities and the USDA

  3. Take action to rectify inequalities in program access through targeted outreach, training, and technical assistance.

The results of this work are intended to be used to improve access and reduce barriers for all applicants, presumably part of the agency’s renewed efforts to address USDA’s history of systemic discrimination.4

Community preparedness

Recent research suggests that the success of food system interventions, policies, and strategies for local economic development may hinge on the preexisting levels of community capital.5

Additional research showed positive associations between cultural and social capital and farm to school activity.6

Much of this research highlights community assets that are often overlooked in community development work.7

Objective

This report intends to lay the groundwork for an analytic approach that helps determine which community characteristics are associated with LAMP grant funding allocation. This could help determine if there is something akin to a “threshold of community preparedness” the unknowingly results in certain low-resource communities being excluded from LAMP programming. If so, the results of this research could provide insight into the particular characteristics associated with LAMP access, which could help agency staff to better allocate resources to ensure equitable access to grant funds.

Methods

Data access and aggregation

As a first step, a variety of data sets were obtained, cleaned, organized, and used for general data exploration. Information on specific datasets and sources can be found below. All work was done using the open source statistical software R version 4.4.0.8

LAMP grant data

Information on LAMP awards came from the LAMP Navigator website, where AMS has made this information publicly available, along with a dashboard for sorting, filtering, and visualizing the grant information.9 Along with information about the organizations receiving the grant, the dataset includes information on the purpose of the grant (e.g., technical assistance, infrastructure, processing), the match amount, and the total project cost.

LAMP grant award amounts, 2006 - 2023
Each green dash represents a single grant award

Geographic distribution of LAMP Grants, 2006-2023

Community characteristics

A variety of socioeconomic and environmental factors were investigated to assess how they may influence the likelihood of receiving a LAMP grant. These factors include indicators of community wealth, which encompasses social capital, natural capital, financial capital, and a variety of other forms of wealth, which have been shown impacts the ability to engage and participate in such programs.10 Additionally, it includes factors related to poverty and food security, which have been shown to exacerbate vulnerabilities and influence accessibility and participation in programs.11 Finally, considering the food systems-focus of LAMP, factors related to urbanization and proximity to agricultural land were included because they can influence market dynamics and food system connectivity.12

Indicators of community wealth

Community wealth data were accessed via the USDA AMS Data and Metrics GitHub repository.13 The main source of data was the “Indicators of Community Wealth” dataset within this repository, which was the result of various pre-processing steps that are outlined within the Rmarkdown file included in the repo.

Indicators of community wealth variables
Descriptions and sources of data used in analysis
Description Data Source
Food Access
food_secure Percentage of the population defined as food secure Derived from USDA data
Processing & Distribution
foodbev_est_CBP NA NA
est_CBP NA NA
Community Characteristics
highway_km NA NA
broad_16 NA NA
pc1b_manufacturing NA NA
pc2b_infrastructure NA NA
create_indus Number of creative industry businesses per 100,000 people Derived from NAICS data
racial_div Racial/ethnic diversity index based on six ethnic categories tracked by the U.S. Census U.S. Census data
pub_lib Number of public libraries per 100,000 people Derived from NAICS 519120
create_jobs Percentage of workers employed in the arts Derived from NAICS 7111 and 7113–7115
museums Number of museums per 100,000 people Derived from NAICS 712110
pc1c_artsdiversity NA NA
pc2c_creativeindustries NA NA
localgovfin County government cash and security holdings net of government debt per capita Derived from local government financial reports
owner_occupied Number of owner-occupied units without a mortgage per capita U.S. Census data
deposits Level of deposits to FDIC-insured institutions per capita FDIC data
pc1f NA NA
ed_attain Percentage of the adult population with a bachelor’s, graduate, or professional degree U.S. Census data
insured Percentage of the population having health insurance Derived from U.S. Census data
primary_care Number of primary care physicians per 10,000 people Medical association data
pc1h_healtheducation NA NA
pc2h_medicalfoodsecurity NA NA
natamen_scale NA NA
prime_farmland Percentage of acres defined as prime farmland National Agricultural Statistics Service
conserve_acre NA NA
acre_FSA NA NA
acre_NFS NA NA
pc1n_naturalamenitiesconservation NA NA
pc2n_farmland NA NA
pvote NA NA
nccs NA NA
assn NA NA
respn NA NA
pc1s_nonprofitsocialindustries NA NA
pc2s_publicvoiceparticipation NA NA
health_factors NA NA
health_outcomes NA NA

Population drivers

Farmland proportion

Underserved classification

(Caveats and such)

  • Temporal component -

  • Does not include data on who applied, in addition to who was funded.

Distribution of LAMP Grant funding across CONUS

Indicators of community wealth

Exploratory map of community wealth data

Additional explanatory variables

Rural-Urban Continuum Classification
Exploratory map of additional explanatory variables

Community characteristics and LAMP funding

Plotting each variable against total LAMP award money received

Dimension reduction with PCA

Started with 44 variables, which were reduced to 10 using a principal components analysis.

Principal components

Cluster analysis

Cluster exploration

Dimension reduction

Use principal component analysis (PCA) to reduce dimensionality of datasets and retain only most important information.

Regression


z test of coefficients:

                                        Estimate     Std. Error z value
(Intercept)                         -20.28431871     4.25780271 -4.7640
acre_FSA                             73.49117181    15.41335917  4.7680
acre_NFS                             -0.39359117     0.51746516 -0.7606
assn                                  0.01128352     0.18927459  0.0596
broad_16                              0.09611833     0.36025942  0.2668
conserve_acre                        -5.91437460     1.81463174 -3.2593
create_indus                          0.00115598     0.00088926  1.2999
create_jobs                           2.40326557     1.15134066  2.0874
deposits                             -0.00062708     0.00032305 -1.9411
ed_attain                             5.18251487     0.58073119  8.9241
est_CBP                               0.01287148     0.00723736  1.7785
food_secure                          -1.34005203     1.55904580 -0.8595
foodbev_est_CBP                       0.05872794     0.02164486  2.7133
health_factors                        0.07735780     0.15126132  0.5114
health_outcomes                       0.36061986     0.10325388  3.4926
highway_km                            0.45797532     0.82081735  0.5580
insured                              -0.92336760     0.95912866 -0.9627
localgovfin                           0.03093457     0.02079570  1.4875
museums                              -0.00449038     0.00309363 -1.4515
natamen_scale                         0.44626825     0.07083544  6.3001
nccs                                  0.01450916     0.01478179  0.9816
owner_occupied                       -4.76002637     1.40778974 -3.3812
pc1b_manufacturing                   -0.03268498     0.02848761 -1.1473
pc1c_artsdiversity                    0.05373925     0.02394341  2.2444
pc1f                                 -0.00596504     0.01189368 -0.5015
pc1h_healtheducation                 -0.05132197     0.01119821 -4.5831
pc1n_naturalamenitiesconservation     0.00364453     0.01042379  0.3496
pc1s_nonprofitsocialindustries       -0.03769158     0.05244313 -0.7187
pc2b_infrastructure                   0.01094557     0.01845864  0.5930
pc2c_creativeindustries              -0.00774709     0.01205048 -0.6429
pc2h_medicalfoodsecurity              0.03140819     0.00694710  4.5211
pc2n_farmland                         2.21194496     0.41604291  5.3166
pc2s_publicvoiceparticipation         0.01495997     0.01639129  0.9127
primary_care                          0.02883013     0.01508162  1.9116
prime_farmland                    -3654.46769681   677.41081854 -5.3948
pub_lib                              -0.00475371     0.00278807 -1.7050
pvote                                -1.02570149     0.90113408 -1.1382
racial_div                            0.05082283     0.02290700  2.2187
respn                                -0.13135141     1.62677819 -0.0807
poverty_rate                          0.05762714     0.00895585  6.4346
ag_proportion                        -0.00142657     0.00155040 -0.9201
RUCC_20232                            0.19165093     0.08438367  2.2712
RUCC_20233                           -0.10185710     0.09706152 -1.0494
RUCC_20234                           -0.06269665     0.11655304 -0.5379
RUCC_20235                           -0.11125754     0.19032796 -0.5846
RUCC_20236                           -0.15992068     0.12635515 -1.2656
RUCC_20237                           -0.60377716     0.16674096 -3.6210
RUCC_20238                           -0.00513030     0.14544994 -0.0353
RUCC_20239                           -0.33669087     0.17337088 -1.9420
is_rural1                             0.00518617     0.10023201  0.0517
is_underserved1                      -0.40955946     0.17371252 -2.3577
                                               Pr(>|z|)    
(Intercept)                             0.0000018976010 ***
acre_FSA                                0.0000018604729 ***
acre_NFS                                      0.4468877    
assn                                          0.9524626    
broad_16                                      0.7896208    
conserve_acre                                 0.0011170 ** 
create_indus                                  0.1936253    
create_jobs                                   0.0368554 *  
deposits                                      0.0522423 .  
ed_attain                         < 0.00000000000000022 ***
est_CBP                                       0.0753256 .  
food_secure                                   0.3900463    
foodbev_est_CBP                               0.0066627 ** 
health_factors                                0.6090582    
health_outcomes                               0.0004784 ***
highway_km                                    0.5768783    
insured                                       0.3356905    
localgovfin                                   0.1368704    
museums                                       0.1466425    
natamen_scale                           0.0000000002975 ***
nccs                                          0.3263187    
owner_occupied                                0.0007217 ***
pc1b_manufacturing                            0.2512410    
pc1c_artsdiversity                            0.0248049 *  
pc1f                                          0.6159979    
pc1h_healtheducation                    0.0000045824086 ***
pc1n_naturalamenitiesconservation             0.7266122    
pc1s_nonprofitsocialindustries                0.4723176    
pc2b_infrastructure                           0.5531957    
pc2c_creativeindustries                       0.5202982    
pc2h_medicalfoodsecurity                0.0000061532693 ***
pc2n_farmland                           0.0000001057084 ***
pc2s_publicvoiceparticipation                 0.3614121    
primary_care                                  0.0559266 .  
prime_farmland                          0.0000000686158 ***
pub_lib                                       0.0881907 .  
pvote                                         0.2550228    
racial_div                                    0.0265099 *  
respn                                         0.9356461    
poverty_rate                            0.0000000001238 ***
ag_proportion                                 0.3575078    
RUCC_20232                                    0.0231358 *  
RUCC_20233                                    0.2939905    
RUCC_20234                                    0.5906297    
RUCC_20235                                    0.5588456    
RUCC_20236                                    0.2056404    
RUCC_20237                                    0.0002934 ***
RUCC_20238                                    0.9718629    
RUCC_20239                                    0.0521339 .  
is_rural1                                     0.9587346    
is_underserved1                               0.0183893 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

z test of coefficients:

                                        Estimate     Std. Error z value
(Intercept)                         -36.86967061     8.91258975 -4.1368
acre_FSA                            134.18418762    33.36357770  4.0219
acre_NFS                             -0.81503904     0.88406519 -0.9219
assn                                 -0.11642148     0.29493469 -0.3947
broad_16                             -0.05339317     0.64388314 -0.0829
conserve_acre                       -10.95868457     3.51850716 -3.1146
create_indus                          0.00248037     0.00152984  1.6213
create_jobs                           3.92917078     2.06383807  1.9038
deposits                             -0.00116315     0.00056179 -2.0704
ed_attain                             9.24595060     1.05657107  8.7509
est_CBP                               0.01899020     0.01272270  1.4926
food_secure                          -2.21018484     2.75163037 -0.8032
foodbev_est_CBP                       0.10883589     0.04053002  2.6853
health_factors                        0.13605603     0.28407699  0.4789
health_outcomes                       0.64813076     0.19573500  3.3113
highway_km                            0.49726356     1.47504198  0.3371
insured                              -1.73486054     1.70160175 -1.0195
localgovfin                           0.05894336     0.03819901  1.5431
museums                              -0.00805239     0.00574408 -1.4019
natamen_scale                         0.77516333     0.15171609  5.1093
nccs                                  0.01727402     0.02183583  0.7911
owner_occupied                       -8.59257724     2.44641387 -3.5123
pc1b_manufacturing                   -0.06365814     0.05720490 -1.1128
pc1c_artsdiversity                    0.08808277     0.04702568  1.8731
pc1f                                 -0.00754781     0.01877476 -0.4020
pc1h_healtheducation                 -0.09344511     0.02168063 -4.3101
pc1n_naturalamenitiesconservation     0.01088413     0.01776812  0.6126
pc1s_nonprofitsocialindustries       -0.03344662     0.07933389 -0.4216
pc2b_infrastructure                   0.02722195     0.03314419  0.8213
pc2c_creativeindustries              -0.01212237     0.02144231 -0.5653
pc2h_medicalfoodsecurity              0.05418948     0.01204903  4.4974
pc2n_farmland                         4.02218949     0.91012784  4.4194
pc2s_publicvoiceparticipation         0.02020364     0.02585123  0.7815
primary_care                          0.05986932     0.02604240  2.2989
prime_farmland                    -6665.53297847  1480.37482877 -4.5026
pub_lib                              -0.00800749     0.00530879 -1.5083
pvote                                -2.04562687     1.62494199 -1.2589
racial_div                            0.08053378     0.04146002  1.9424
respn                                 0.12432517     2.55606577  0.0486
poverty_rate                          0.10027354     0.01599439  6.2693
ag_proportion                        -0.00246169     0.00277311 -0.8877
RUCC_20232                            0.31350997     0.15047077  2.0835
RUCC_20233                           -0.19459866     0.17149872 -1.1347
RUCC_20234                           -0.11068208     0.20361076 -0.5436
RUCC_20235                           -0.22855867     0.32302793 -0.7076
RUCC_20236                           -0.28944835     0.22307217 -1.2976
RUCC_20237                           -1.07740959     0.29360969 -3.6695
RUCC_20238                           -0.04081095     0.26181325 -0.1559
RUCC_20239                           -0.62820549     0.31106053 -2.0196
is_rural1                             0.03834767     0.17672931  0.2170
is_underserved1                      -0.88091389     0.34312727 -2.5673
                                               Pr(>|z|)    
(Intercept)                             0.0000352170884 ***
acre_FSA                                0.0000577363406 ***
acre_NFS                                      0.3565693    
assn                                          0.6930374    
broad_16                                      0.9339122    
conserve_acre                                 0.0018420 ** 
create_indus                                  0.1049476    
create_jobs                                   0.0569340 .  
deposits                                      0.0384119 *  
ed_attain                         < 0.00000000000000022 ***
est_CBP                                       0.1355358    
food_secure                                   0.4218433    
foodbev_est_CBP                               0.0072461 ** 
health_factors                                0.6319808    
health_outcomes                               0.0009287 ***
highway_km                                    0.7360278    
insured                                       0.3079440    
localgovfin                                   0.1228162    
museums                                       0.1609576    
natamen_scale                           0.0000003233511 ***
nccs                                          0.4288936    
owner_occupied                                0.0004442 ***
pc1b_manufacturing                            0.2657904    
pc1c_artsdiversity                            0.0610576 .  
pc1f                                          0.6876700    
pc1h_healtheducation                    0.0000163200322 ***
pc1n_naturalamenitiesconservation             0.5401640    
pc1s_nonprofitsocialindustries                0.6733221    
pc2b_infrastructure                           0.4114647    
pc2c_creativeindustries                       0.5718371    
pc2h_medicalfoodsecurity                0.0000068785451 ***
pc2n_farmland                           0.0000098990155 ***
pc2s_publicvoiceparticipation                 0.4344881    
primary_care                                  0.0215096 *  
prime_farmland                          0.0000067127734 ***
pub_lib                                       0.1314658    
pvote                                         0.2080693    
racial_div                                    0.0520834 .  
respn                                         0.9612068    
poverty_rate                            0.0000000003627 ***
ag_proportion                                 0.3747009    
RUCC_20232                                    0.0372032 *  
RUCC_20233                                    0.2565033    
RUCC_20234                                    0.5867192    
RUCC_20235                                    0.4792243    
RUCC_20236                                    0.1944404    
RUCC_20237                                    0.0002430 ***
RUCC_20238                                    0.8761291    
RUCC_20239                                    0.0434290 *  
is_rural1                                     0.8282198    
is_underserved1                               0.0102491 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

z test of coefficients:

                    Estimate Std. Error z value              Pr(>|z|)    
(Intercept)        -0.591811   0.080969 -7.3091    0.0000000000002689 ***
pc1_education       0.249004   0.013610 18.2958 < 0.00000000000000022 ***
pc2_arts            0.154531   0.018021  8.5753 < 0.00000000000000022 ***
pc3_conservation1   0.073741   0.017454  4.2250    0.0000238992055927 ***
pc4_farmland1      -0.025556   0.017903 -1.4275              0.153448    
pc5_infrastructure -0.252354   0.029673 -8.5045 < 0.00000000000000022 ***
pc6_farmland2      -0.058473   0.022534 -2.5948              0.009464 ** 
pc7_manufacturing  -0.128697   0.051052 -2.5209              0.011706 *  
pc8_conservation2   0.049327   0.029026  1.6994              0.089240 .  
pc9_foodbev        -0.046553   0.041133 -1.1318              0.257727    
pc10_civics        -0.056799   0.059744 -0.9507              0.341749    
RUCC_20232          0.229706   0.076450  3.0046              0.002659 ** 
RUCC_20233          0.033948   0.085853  0.3954              0.692537    
RUCC_20234          0.183068   0.108047  1.6943              0.090200 .  
RUCC_20235          0.088727   0.181497  0.4889              0.624938    
RUCC_20236         -0.067042   0.119182 -0.5625              0.573765    
RUCC_20237         -0.355806   0.158260 -2.2482              0.024561 *  
RUCC_20238          0.110877   0.137360  0.8072              0.419553    
RUCC_20239         -0.115377   0.162806 -0.7087              0.478523    
is_rural1          -0.018903   0.096045 -0.1968              0.843975    
is_underserved1    -0.274512   0.149605 -1.8349              0.066519 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Footnotes

  1. https://www.ams.usda.gov/sites/default/files/media/LAMP_Report_to_Congress.pdf↩︎

  2. https://www.ams.usda.gov/services/grants/lamp. Accessed April 20, 2024↩︎

  3. https://www.ams.usda.gov/sites/default/files/media/MSDUSDAAMSGrantApplicantTASociallyDisadvantaged.pdf↩︎

  4. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  5. Schmit, Todd M., Becca B.R. Jablonski, Alessandro Bonanno, and Thomas G. Johnson. “Measuring Stocks of Community Wealth and Their Association with Food Systems Efforts in Rural and Urban Places.” Food Policy 102 (July 2021): 102119. https://doi.org/10.1016/j.foodpol.2021.102119.↩︎

  6. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  7. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  8. https://www.r-project.org/↩︎

  9. https://www.ams.usda.gov/data/lamp-navigator↩︎

  10. Flora, Cornelia Butler, Jan L. Flora, and Stephen P. Gasteyer. Rural Communities: Legacy and Change. 4th ed. Routledge, 2018. https://doi.org/10.4324/9780429494697.↩︎

  11. Alisha Coleman-Jensen, Matthew P. Rabbitt, Christian A. Gregory, and Anita Singh. 2021. Household Food Security in the United States in 2020, ERR-298, U.S. Department of Agriculture, Economic Research Service.↩︎

  12. Pothukuchi, Kameshwari, and Jerome L. Kaufman. “The Food System: A Stranger to the Planning Field.” Journal of the American Planning Association 66, no. 2 (June 30, 2000): 113–24. https://doi.org/10.1080/01944360008976093.↩︎

  13. https://github.com/CSU-Local-and-Regional-Food-Systems/USDA-AMS-Data-and-Metrics/tree/main↩︎